One-class acoustic characterization applied to blood detection in IVUS.

Intravascular ultrasound (IVUS) is an invasive imaging modality capable of providing cross-sectional images of the interior of a blood vessel in real time and at normal video framerates (10-30 frames/s). Low contrast between the features of interest in the IVUS imagery remains a confounding factor in IVUS analysis; it would be beneficial therefore to have a method capable of detecting certain physical features imaged under IVUS in an automated manner. We present such a method and apply it to the detection of blood. While blood detection algorithms are not new in this field, we deviate from traditional approaches to IVUS signal characterization in our use of 1-class learning. This eliminates certain problems surrounding the need to provide "foreground" and "background" (or, more generally, n-class) samples to a learner. Applied to the blood-detection problem on 40 MHz recordings made in vivo in swine, we are able to achieve approximately 95% sensitivity with approximately 90% specificity at a radial resolution of approximately 600 microm.